Industry increasingly depends upon highly automated data acquisition and control systems to ensure that industrial processes are run efficiently, safely and reliably while lowering their overall production costs. Data acquisition begins when a number of sensors measure aspects of an industrial process and periodically report their measurements back to a data collection and control system. Such measurements come in a wide variety of forms. By way of example the measurements produced by a sensor/recorder include: a temperature, a pressure, a pH, a mass/volume flow of material, a tallied inventory of packages waiting in a shipping line, or a photograph of a room in a factory. Often sophisticated process management and control software examines the incoming data, produces status reports, and, in many cases, responds by sending commands to actuators/controllers that adjust the operation of at least a portion of the industrial process. The data produced by the sensors also allow an operator to perform a number of supervisory tasks including: tailor the process control (e.g., specify new set points) in response to varying external conditions (including costs of raw materials), detect an inefficient/non-optimal operating condition and/or impending equipment failure, and take remedial actions such as move equipment into and out of service as required.
Typical industrial processes are extremely complex and receive substantially greater volumes of information than any human could possibly digest in its raw form. By way of example, it is not unheard of to have thousands of sensors and control elements (e.g., valve actuators) monitoring/controlling aspects of a multi-stage process within an industrial plant. These sensors are of varied type and report on varied characteristics of the process. Their outputs are similarly varied in the meaning of their measurements, in the amount of data sent for each measurement, and in the frequency of their measurements. As regards the latter, for accuracy and to enable quick response, some of these sensors/control elements take one or more measurements every second. When multiplied by thousands of sensors/control elements, so much data flows into the process control system that sophisticated data management and process visualization techniques are required.
Highly advanced human-machine interface/process visualization systems exist today that are linked to data sources such as the above-described sensors and controllers. Such systems acquire and digest (e.g., filter) the process data described above. The digested process data in-turn drives a graphical display rendered by a workstation's human machine interface. Such data includes mode changes, events, and alarm messages rendered by process controllers in response to a variety of detected process conditions/circumstances.
Process data is traditionally sent from plant controllers to operator displays on workstations and historical archives for trending and data collection purposes. Generally plant controllers have a block processing periods of 100 ms or more. This is acceptable for normal plant control purposes since most processes do not change greatly over such periods. However, when a process experiences a fault leading to, for example, a safety shutdown, the 100 ms period potentially prevents capture of digital state changes that would enable an operator to determine a source of a fault. Instead a number of faults will potentially have an identical time stamp—corresponding to the last block processing cycle. There is no way to discern from the time stamps the order in which a set of faults occurred. However, more recently control processors have been introduced that timestamp data wherein the granularity and degree of synchronization of assigned times are on the order of one ms. Such information can potentially be used by supervisory level systems to generate/provide a progression order of a sequence of faults in a system.
Many industries, including for example the power generation industries (both nuclear and non-nuclear), have a need to capture digital data at high speed (in very small time increments) for safety and/or environmental incident management reports. In the case of the nuclear power industry, such reports have been mandatory for over 30 years. If data is grouped in accordance with a block execution cycle, then a set of cascading process/equipment failures is likely to register as a set of simultaneous failures (the timestamp associated with a block cycle period within which the set of faults occurred). The ability to generate meaningful incident reports is potentially hampered by an inability to identify the actual temporal ordering of a set of related faults that occurred in a very short time period.
Another aspect of determining the cause(s) of a malfunction or shutdown is ensuring that sufficient process data points are tracked to enable a cause of a failure to be identified. Thus, devices are being used to track an increasing number of data points to provide greater granularity for process status views. The combination of more data points and greater timestamp granularity results in potentially much larger bursts of data from a system during failure or other exceptional circumstances. During a large-scale cascading set of failures in a process control system, a potentially very large volume of event data is rendered in a very short period time. As used herein, an “event” corresponds to a change in state of a digital input signal (e.g., on/off, open/closed, etc.). When a digital input goes from false to true (or true to false), an event is registered and an appropriate timestamped message is issued to appropriate subscribers to the event. A specialized facility, referred to as a sequence of events (SOE) database documents/records, with a higher degree of granularity, the order in which digital data, corresponding to particular events (e.g., disturbances, alarms, exceptions, etc.), is registered in a control system. Another challenge in complex distributed control systems is synchronizing the components that assign a timestamp to an event. Thus, it is desirable to provide a system for supporting the recording of a potentially very large number of events generated at a high frequency by distributed components in a process control environment with a high degree of granularity and a high degree of synchronicity.